Current troubleshooting techniques employ inadequate checklists and one-sided diagnostic testing sessions. Indeed, the processes of a user performing trial and error troubleshooting without expert advice and a technician following pedantic and time-wasting checklists without feedback from a user are a huge waste of time.
Known troubleshooting systems do not integrate machine knowledge with human interaction with an electronic device. Additionally, known systems do not integrate past experiences learned from interaction with a large distributed userbase with a system that can learn from those experiences.
There is a need in the art for high quality and inexpensive troubleshooting that combines artificial intelligence, machine learning, human feedback, and intelligent optimization algorithms into a cloud-based platform serving and learning from a distributed userbase.